Leveraging Data Collection and Unsupervised Learning for Code-switched Tunisian Arabic Automatic Speech Recognition

Crafting an effective Automatic Speech Recognition (ASR) solution for dialects demands innovative approaches that not only address the data scarcity issue but also navigate the intricacies of linguistic diversity. In this paper, we address the aforementioned ASR challenge, focusing on the Tunisian dialect. First, textual and audio data is collected and in some cases annotated. Second, we explore self-supervision, semi-supervision and few-shot code-switching approaches to push the state-of-the-art on different Tunisian test sets; covering different acoustic, linguistic and prosodic conditions. Finally, and given the absence of conventional spelling, we produce a human evaluation of our transcripts to avoid the noise coming from spelling inadequacies in our testing references. Our models, allowing to transcribe audio samples in a linguistic mix involving Tunisian Arabic, English and French, and all the data used during training and testing are released for public use and further improvements.Comment: 6 pages, submitted to ICASSP 202

A review of sentiment analysis research in Arabic language

Sentiment analysis is a task of natural language processing which has recently attracted increasing attention. However, sentiment analysis research has mainly been carried out for the English language. Although Arabic is ramping up as one of the most used languages on the Internet, only a few studies have focused on Arabic sentiment analysis so far. In this paper, we carry out an in-depth qualitative study of the most important research works in this context by presenting limits and strengths of existing approaches. In particular, we survey both approaches that leverage machine translation or transfer learning to adapt English resources to Arabic and approaches that stem directly from the Arabic language

Atar: Attention-based LSTM for Arabizi transliteration

A non-standard romanization of Arabic script, known as Arbizi, is widely used in Arabic online and SMS/chat communities. However, since state-of-the-art tools and applications for Arabic NLP expects Arabic to be written in Arabic script, handling contents written in Arabizi requires a special attention either by building customized tools or by transliterating them into Arabic script. The latter approach is the more common one and this work presents two significant contributions in this direction. The first one is to collect and publicly release the first large-scale “Arabizi to Arabic script” parallel corpus focusing on the Jordanian dialect and consisting of more than 25 k pairs carefully created and inspected by native speakers to ensure highest quality. Second, we present Atar, an attention-based encoder-decoder model for Arabizi transliteration. Training and testing this model on our dataset yields impressive accuracy (79%) and BLEU score (88.49)

Maghrebi Arabic dialect processing: an overview

International audienceNatural Language Processing for Arabic dialects has grown widely these last years. Indeed, several works were proposed dealing with all aspects of Natural Language Processing. However , some AD varieties have received more attention and have a growing collection of resources. Others varieties, such as Maghrebi, still lag behind in that respect. Maghrebi Arabic is the family of Arabic dialects spoken in the Maghreb region (principally Algeria, Tunisia and Morocco). In this work we are interested in these three languages. This paper presents a review of natural language processing for Maghrebi Arabic dialects

A hybrid approach based on personality traits for hate speech detection in Arabic social media

In recent years, as social media has grown in popularity, people have gained the ability to freely share their views. However, this may lead to users' conflict and hostility, resulting in unattractive online environments. Hate speech relates to using expressions or phrases that are violent, offensive, or insulting to a minority of people. The number of Arab social media users is quickly rising, and this is being followed by an increase in the frequency of cyber hate speech in the area. Therefore, the automated detection of Arabic hate speech has become a major concern for many stakeholders. The intersection of personality learning and hate speech detection is a relatively less studied niche. We suggest a novel approach that is focused on extracting personality trait features and using these features to detect Arabic hate speech. The experimental results show that the proposed approach is superior in terms of the macro-F1 score by achieving 82.3% compared to previous work reported in the literature

The Effects of Factorizing Root and Pattern Mapping in Bidirectional Tunisian - Standard Arabic Machine Translation

International audienceThe development of natural language processing tools for dialects faces the severe problem of lack of resources. In cases of diglossia, as in Arabic, one variant, Modern Standard Arabic (MSA), has many resources that can be used to build natural language processing tools. Whereas other variants, Arabic dialects, are resource poor. Taking advantage of the closeness of MSA and its dialects, one way to solve the problem of limited resources, consists in performing a translation of the dialect into MSA in order to use the tools developed for MSA. We describe in this paper an architecture for such a translation and we evaluate it on Tunisian Arabic verbs. Our approach relies on modeling the translation process over the deep morphological representations of roots and patterns, commonly used to model Semitic morphology. We compare different techniques for how to perform the cross-lingual mapping. Our evaluation demonstrates that the use of a decent coverage root+pattern lexicon of Tunisian and MSA with a backoff that assumes independence of mapping roots and patterns is optimal in reducing overall ambiguity and increasing recall